使用R语言做机器学习的书籍推荐

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总是有一些小伙伴觉得机器学习很高大上，令人望而生畏，其实它就是我们常见的统计学方法，比如做表达量矩阵分析，通常是需要绘制pca图看看组间差异是否足够明显。

如果你有单细胞转录组数据处理经验，实际上流程里面的降维聚类分群无一不是机器学习。如果你做肿瘤数据挖掘，经常会使用lasso，随机森林，支持向量机，它们都是在R里面非常容易实现。我们也多次推荐过 《精通机器学习:基于R(第2版)-图书-图灵社区》：https://www.ituring.com.cn/book/1989 （赠书活动）

如果你不想看中文书籍

有意思的是一些小伙伴对中文翻译比较抵触，喜欢看英文原版，我们也有推荐：

在线书籍地址：https://f0nzie.github.io/machine_learning_compilation/index.html

目录

  • 1 Preface

The Basics of Machine Learning

  • 2 Introduction to PCA
  • 3 Comparison of two PCA packages
  • 4 Detailed study of Principal Component Analysis
  • 5 Detection of diabetes using Logistic Regression
  • 6 Sensitivity analysis for a neural network
  • 7 Data Visualization for ML models

Feature Engineering

  • 8 Ten methods to assess Variable Importance
  • 9 Employee Attrition using Feature Importance

Classification

  • 10 A gentle introduction to Support Vector Machines
  • 11 Broad view of SVM
  • 12 Feature Selection to enhance cancer detection
  • 13 Dealing with unbalanced data
  • 14 Imputting missing values with Random Forest
  • 15 Tuning of Support Vector Machine prediction

Classification

  • 16 Introduction to algorithms for Classification
  • 17 Comparing Classification algorithms
  • 18 Who buys Social Network ads
  • 19 Predicting Ozone levels
  • 20 Building a Naive Bayes Classifier
  • 21 Linear and Non-Linear Algorithms for Classification
  • 22 Detect mines vs rocks with Random Forest
  • 23 Predicting the type of glass
  • 24 Naive Bayes for SMS spam
  • 25 Vehicles classiification with Decision Trees
  • 26 Applying Naive-Bayes on the Titanic case
  • 27 Classification on bad loans
  • 28 Predicting Flu outcome comparing eight classification algorithms
  • 29 A detailed study of bike sharing demand
  • 30 Prediction of arrhythmia with deep neural nets

Linear Regression

  • 31 Linear Regression with ISLR
  • 32 Evaluation of three linear regression models
  • 33 Comparison of six Linear Regression algorithms
  • 34 Comparing regression models
  • 35 Finding the factors of happiness
  • 36 Regression with a neural network
  • 37 Comparing Multiple Regression vs a Neural Network
  • 38 Temperature modeling using nested dataframes

Neural Networks

  • 39 Credit Scoring with neuralnet
  • 40 Wine classification with neuralnet
  • 41 Predicting the rating of cereals
  • 42 Fitting a linear model with neural networks
  • 43 Visualization of neural networks
  • 44 Build a fully connected R neural network from scratch
  • 45 Tuning Hyperparameters in a Neural Network
  • 46 Deep Learning tips for Classification and Regression

Appendix

  • A What is dot hat in a regression output
  • B Q-Q normal to compare data to distributions
  • C QQ and PP Plots
  • D Visualizing residuals

书籍可能没有视频动画更加通俗易懂

StatQuest生物统计学视频是一个很优秀的生物统计学教程，教程作者是Josh Starmer （个人博客https://statquest.org/），生信菜鸟图很早之前就推过相关的学习资源。而且还组建过学习小分队，给视频写配套笔记：

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